[Exploratory Data Analysis] Project 1

Project summary

This assignment uses data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. In particular, we will be using the “Individual household electric power consumption Data Set” which I have made available on the course web site:

• Dataset: Electric power consumption [20Mb]

• Description: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.
  The following descriptions of the 9 variables in the dataset are taken from the UCI web site:

  1. Date: Date in format dd/mm/yyyy
  2. Time: time in format hh:mm:ss
  3. Global_active_power: household global minute-averaged active power (in kilowatt)
  4. Global_reactive_power: household global minute-averaged reactive power (in kilowatt)
  5. Voltage: minute-averaged voltage (in volt)
     6 Global_intensity: household global minute-averaged current intensity (in ampere)
  6. Sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).
  7. Sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.
  8. Sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

Review criteria

1. Was a valid GitHub URL containing a git repository submitted?
2. Does the GitHub repository contain at least one commit beyond the original fork?
3. Please examine the plot files in the GitHub repository. Do the plot files appear to 4. be of the correct graphics file format?
4. Does each plot appear correct?
5. Does each set of R code appear to create the reference plot?

Loading the data

When loading the dataset into R, please consider the following:

• The dataset